Fetching the paper…
Reading the bibliography…
Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits.
S. Hrabar, “3d path planning and stereo-based obstacle avoidance for rotorcraft uavs,” in International Conference on Intelligent Robots and Systems (IROS) , 2008, pp. 807–814
2008
Earlier work this paper cites.
W. Xu, J. Pan, J. Wei, and J. M. Dolan, “Motion planning under uncertainty for on-road autonomous driving,” in International Conference on Robotics and Automation (ICRA) , 2014
2014
Earlier work this paper cites.
Y. Gal et al. , “Uncertainty in deep learning,” 2016
2016
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles,” in Neural Information Processing Systems (NIPS) , 2017
2017
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” in International Conference on Machine Learning (ICML) , 2017
2017
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles,” in Neural Information Processing Systems (NIPS) , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in International Conference on Computer Vision (ICCV) , 2017
2017
Earlier work this paper cites.
A. Kendall and Y. Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” in Neural Information Processing Systems (NIPS) , 2017
2017
Earlier work this paper cites.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in Proceedings of the 34th International Conference on Machine Learning - Volume 70 , ser. ICML’17. JMLR.org, 2017, p. 1321–1330
2017
Earlier work this paper cites.
W. H. Beluch, T. Genewein, A. Nurnberger, and J. M. Kohler, “The power of ensembles for active learning in image classification,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 9368–9377
2018
Earlier work this paper cites.
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral Normalization for Generative Adversarial Networks,” in International Conference on Learning Representations (ICLR) , 2018
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
D. Feng, A. Harakeh, S. Waslander, and K. Dietmayer, “A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving,” in IEEE Transactions on Intelligent Transportation Systems (ITS) , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Van Amersfoort, L. Smith, Y. W. Teh, and Y. Gal, “Uncertainty Estimation Using a Single Deep Deterministic Neural Network,” in International Conference on Machine Learning (ICML) , 2020
2020
Cited alongside, same era.
J. R. van Amersfoort, L. Smith, Y. W. Teh, and Y. Gal, “Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network,” in International Conference on Machine Learning (ICML) , 2020
2020
Cited alongside, same era.
J. Liu, Z. Lin, S. Padhy, D. Tran, T. Bedrax Weiss, and B. Lakshminarayanan, “Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness,” in Neural Information Processing Systems (NIPS) , 2020
2020
Cited alongside, same era.
J. Philion and S. Fidler, “Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D,” in European Conference on Computer Vision (ECCV) , 2020
2020
Cited alongside, same era.
Y. Wei, L. Zhao, W. Zheng, Z. Zhu, J. Zhou, and J. Lu, “Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving,” in International Conference on Computer Vision (ICCV) , 2023
2023
Later among the works it cites.
Y. Huang, W. Zheng, Y. Zhang, J. Zhou, and J. Lu, “Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Li, Z. Yu, C. Choy, C. Xiao, J. M. Alvarez, S. Fidler, C. Feng, and A. Anandkumar, “VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuScenes: A Multimodal Dataset for Autonomous Driving,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Cited alongside, same era.
C. Sakaridis, D. Dai, and L. Van Gool, “ACDC: The adverse conditions dataset with correspondences for semantic driving scene understanding,” in International Conference on Computer Vision (ICCV) , October 2021
2021
Cited alongside, same era.
M. Havasi, R. Jenatton, S. Fort, J. Z. Liu, J. Snoek, B. Lakshminarayanan, A. M. Dai, and D. Tran, “Training independent subnetworks for robust prediction,” in International Conference on Learning Representations (ICLR) , 2021
2021
Cited alongside, same era.
N. Durasov, T. Bagautdinov, P. Baque, and P. Fua, “Masksembles for Uncertainty Estimation,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Cited alongside, same era.
N. Durasov, T. Bagautdinov, P. Baque, and P. Fua, “Masksembles for Uncertainty Estimation,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
A.-Q. Cao and R. de Charette, “MonoScene: Monocular 3D Semantic Scene Completion,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Cited alongside, same era.
K. Wang, Y. Wang, B. Liu, and J. Chen, “Quantification of uncertainty and its applications to complex domain for autonomous vehicles perception system,” IEEE Transactions on Instrumentation and Measurement , vol. 72, pp. 1–17, 2023
2023
Later among the works it cites.
L. Kong, Y. Liu, X. Li, R. Chen, W. Zhang, J. Ren, L. Pan, K. Chen, and Z. Liu, “Robo3d: Towards robust and reliable 3d perception against corruptions,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2023, pp. 19 994–20 006
2023
Later among the works it cites.
J. Mukhoti, A. Kirsch, J. van Amersfoort, P. H. Torr, and Y. Gal, “Deep Deterministic Uncertainty: A New Simple Baseline,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.
O. Laurent, A. Lafage, E. Tartaglione, G. Daniel, J.-M. Martinez, A. Bursuc, and G. Franchi, “Packed-Ensembles for Efficient Uncertainty Estimation,” in International Conference on Learning Representations (ICLR) , 2023
2023
Later among the works it cites.
Y. Zhang, Z. Zhu, and D. Du, “OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction,” in International Conference on Computer Vision (ICCV) , 2023
2023
Later among the works it cites.
Y. Huang, W. Zheng, Y. Zhang, J. Zhou, and J. Lu, “Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Later among the works it cites.
X. Tian, T. Jiang, L. Yun, Y. Mao, H. Yang, Y. Wang, Y. Wang, and H. Zhao, “Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving,” in Neural Information Processing Systems (NIPS) , 2024
2024
Later among the works it cites.
T. Beemelmanns, Q. Zhang, C. Geller, and L. Eckstein, “Multicorrupt: A multi-modal robustness dataset and benchmark of lidar-camera fusion for 3d object detection,” in Intelligent Vehicles Symposium (IV) , 2024
2024
Later among the works it cites.
A.-Q. Cao, A. Dai, and R. de Charette, “PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
T. Yoon and H. Kim, “Uncertainty estimation by density aware evidential deep learning,” in Forty-first International Conference on Machine Learning , 2024
2024
Later among the works it cites.